Minimized-Laplacian residual interpolation for color image demosaicking

Kiku, Daisuke; Monno, Yusuke; Tanaka, Masayuki; Okutomi, Masatoshi

doi:10.1117/12.2038425

Cited by 84 publications

(128 citation statements)

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“…This method has proven to be effective. Due to the prominent impact of a precise tentative estimate on the performance of the algorithm, KiKu et al proposed minimized-Laplacian residual interpolation (MLRI) [12] to tentatively generate a more accurate estimate and introduced a weighted average in the guide filter [13]. Valuable information can be obtained from these two articles.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the interpolation of the full G values significantly contributes to the interpolation of the overall image. This novel approach is inspired by the work in [12,17]. After analyzing those experimental results, the G pixel values in R/B pixels are creatively interpolated by first estimating the G values in eight directions.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2b shows the RI [10] process of the R plane. The part that is different from CDI [12] is the estimations of R pixel values. Firstly, to get the full G images, the subsampled G components are interpolated.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, to generate the full R images, the interpolated G pixel images are added to the interpolated color difference images. [12] and (b) the process of residual interpolation (RI) [10]. Figure 2b shows the RI [10] process of the R plane.…”

Section: Introductionmentioning

confidence: 99%

“…The interpolation of B plane is the same. The first method is the color difference interpolation (CDI) [12], as shown in Figure 2a. Firstly, color difference images are produced by subtracting the interpolated G pixel values from the sample of R pixels.…”

Section: Introductionmentioning

confidence: 99%

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Bayer Image Demosaicking Using Eight-Directional Weights Based on the Gradient of Color Difference

et al. 2018

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Abstract:In this paper, we propose a new demosaicking algorithm which uses eight-directional weights based on the gradient of color difference (EWGCD) for Bayer image demosaicking. To obtain the interpolation of green (G) pixels, the eight-directional G pixel values are first estimated in red (R)/blue (B) pixels. This estimate is used to calculate the color difference in R/B pixels of the Bayer image in diagonal directions. However, in horizontal and vertical directions, the new estimated G pixels are defined to obtain the color difference. The eight-directional weights of estimated G pixels can be obtained by considering the gradient of the color difference and the gradient of the RGB pixels of the Bayer image. Therefore, the eight-directional weighted values and the first estimated G pixel values are combined to obtain the full G image. Compared with six similar algorithms using the same eighteen McMaster images, the results of the experiment demonstrate that the proposed algorithm has a better performance not only in the subjective visual measurement but also in the assessments of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index measurement.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%